Music genre recommendation based on MLP & random forest

Music is the third language of communication between each other in the world. In the process of music development, many music genres have emerged, such as rap and folk music. At present, the method of music recommendation is very mature. Generally, each music app has a function of music recommendation. But there are fewer cases where people are recommended music genres based on certain features. In this paper, a new music genre recommendation method is used to determine the type of music a person likes. This method is based on the actual questionnaire survey made, investigates the basic information and life portrait of each person, and builds a music genre recommendation model based on this information. This paper considers a total of 20 different music genres; and uses MLP and the Random Forest model to design the proposed method. We implement a prototype of our method and evaluate it via our experiments. The experimental evaluation results show that the recommendation accuracy rate of music genres can reach up to 95.47% for Random Forest, which significantly outperforms MLP with a recommendation accuracy rate of only 53.07%.